# MultiEarth 2022 Deforestation Challenge -- ForestGump

arXiv: https://arxiv.org/abs/2206.10831 · HTML: https://mlsanity.com/paper/2206.10831/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)

## Built on by (most cited first, 3 of 3)

- [MultiEarth 2023 Deforestation Challenge -- Team FOREVER](https://mlsanity.com/paper/2306.11762/) (2023)
- [Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery](https://mlsanity.com/paper/2307.04916/) (2023)
- [CVPR MultiEarth 2023 Deforestation Estimation Challenge:SpaceVision4Amazon](https://mlsanity.com/paper/2307.04715/) (2023)

## Abstract (arXiv)

The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become possible. In this paper, we present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.